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一种用于输电线路维护无人机的仿生姿态估计与目标感知策略

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs.

作者信息

Zhuo Haoze, Yang Zhong, Zhang Chi, Xu Nuo, Xue Bayang, Zhu Zekun, Xie Yucheng

机构信息

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530000, China.

出版信息

Biomimetics (Basel). 2024 Dec 6;9(12):745. doi: 10.3390/biomimetics9120745.

DOI:10.3390/biomimetics9120745
PMID:39727749
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11673162/
Abstract

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

摘要

高压架空电力线路作为电力传输的载体，对电力系统的稳定运行至关重要。因此，尽快检测并清除附着在输电线路上的异物尤为重要。在此背景下，智能机器人在电力行业的广泛推广和应用有助于应对该行业日益复杂的挑战，并确保电网系统高效、经济且安全地运行。本文提出了一种基于仿生的无人机姿态估计和目标感知策略，旨在解决传统电力线巡检无人机缺乏模式识别和自动跟踪能力以及视觉里程计鲁棒性差的问题。与现有的无人机环境感知解决方案相比，本文提出的仿生目标感知算法能够从红外图像中高效提取点和线特征，并实现小型多旋翼无人机在电力线场景下的目标检测和自动跟踪功能，且功耗较低。

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